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First Medium article is out!
Come see how Optumi is thinking about the shifting workflow needs of data science and machine learning professionals.
https://medium.com/@optumi/scale-ml-experiments-from-jupyterlab-to-the-cloud-141bd645d8e9
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If you are interested in getting your text converted to an image by Google Brain Imagen use the following link:
https://twitter.com/mo_norouzi/status/1529497457234780162?s=20&t=3K_M972bMeGRR2wG6kobHQ
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This blog post (Optimizing TensorFlow Lite Runtime Memory) says that TensorFlow Lite employs different approaches to handle intermediate tensors which occupy large amounts of memory. Is one of them DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training method?
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https://github.com/visualdatabase/fastdup
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A new training approach yields artificial intelligence that adapts to diverse play-styles in a cooperative game, in what could be a win for human-AI teaming.
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Modeling study suggests that the muffled environment in utero primes the brain’s ability to interpret some types of sound.
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Hi, all,
Glad to share an open source repository PaddleSpeech, which provides SOTA/Streaming ASR witch punctuation, influential TTS with text frontend and a product-ready VPR System.
Code:https://github.com/PaddlePaddle/PaddleSpeech
Features Set:
📦 Ease of Use: low barriers to install. The CLIs are available to quick-start your project.
🔬 Align to the State-of-the-Art: provide high-speed and ultra-lightweight models, and also cutting-edge technology.
🏆 Streaming ASR and TTS System: provide production ready streaming asr and streaming tts system.
💯 Rule-based frontend: the frontend contains Text Normalization and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi).
🛎️ Multi-language: both English and Chinese are supported.
Examples:
Speech Recognition
Input wav: Input.wav
Output text: I knocked at the door on the ancient side of the building.
Text-to-Speech
Input text: Life was like a box of chocolates, you never know what you're gonna get.
Output wav: Output.wav
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https://imagen.research.google/
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TL;DR
We made autoregressive transformer based models like T5-large 2X faster than 🤗 Hugging Face Pytorch with 3 simple tricks:
storing 2 computation graphs in a single Onnx file 👯: this let us have both cache and no cache support without having any duplicated weights. When cache is used, attention switch from quadratic to linear complexity (less GPU computation) and Onnx Runtime brings us kernel fusion (less memory bound ops);
zero copy 💥 to retrieve output from Onnx Runtime: we leverage Cupy API to access Onnx Runtime internal CUDA arrays and expose them through Dlpack to Pytorch. It may sound a bit complex, but it let us avoid output tensors copy which limit our memory footprint and make us much faster (check notebook for other benefits of this approach);
a generic tool to conv…
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In the simple spread env, only position is tracked (https://github.com/openai/multiagent-particle-envs/blob/47e9ee38e605f8a563370b3c7e52a349eca3f6b1/multiagent/scenarios/simple_spread.py#L40)
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Hey guys!
I'm a student and I'm currently working on my dissertation for University. I'm using this as a way of collecting data on the representation of AI in movies and pop culture and I'd appreciate the responses!
Here's the link:
https://forms.gle/1jrzrfuSd3rFD6A17
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Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.
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Digital twins that revolutionize the way the most complex products are produced. Silicon and software that transforms data centers into AI factories. Gaming advances that bring the world’s most popular games to life. Taiwan has become the engine that brings the latest innovations to the world. So it only makes sense that NVIDIA leaders brought Read article >
The post NVIDIA Brings Data Center, Robotics, Gaming, Content Creation Innovations to COMPUTEX appeared first on NVIDIA Blog.
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In the worldwide effort to halt climate change, Zac Smith is part of a growing movement to build data centers that deliver both high performance and energy efficiency. He’s head of edge infrastructure at Equinix, a global service provider that manages more than 240 data centers and is committed to becoming the first in its Read article >
The post NVIDIA Adds Liquid-Cooled GPUs for Sustainable, Efficient Computing appeared first on NVIDIA Blog.
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More than 30 leading technology partners worldwide announced this week the first wave of NVIDIA Jetson AGX Orin-powered production systems at COMPUTEX in Taipei. New products are coming from a dozen Taiwan-based camera, sensor and hardware providers for use in edge AI, AIoT, robotics and embedded applications. Available worldwide since GTC in March, the NVIDIA Read article >
The post NVIDIA Partners Announce Wave of New Jetson AGX Orin Servers and Appliances at COMPUTEX appeared first on NVIDIA Blog.
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The future of content creation was on full display during the virtual NVIDIA keynote at COMPUTEX 2022, as the NVIDIA Studio platform expands with new Studio laptops and RTX-powered AI apps — all backed by the May Studio Driver released today.
The post Master of Arts: NVIDIA RTX GPUs Accelerate Creative Ecosystems, Delivering Unmatched AI and Ray-Tracing Performance appeared first on NVIDIA Blog.
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Delegation meets campus leaders, with an eye toward AI applications and the Icelandic language.
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In recent years, a significant part of neuroscience research has focused on relating deep learning architectures to the human brain, and many deep learning (DL) techniques have recently been shown to replicate neural firing patterns observed in the brain. For example, representations of convolutional neural networks have been shown to predict neurons in the visual cortex and inferior temporal cortex, while recurrent neural networks have been shown to recapitulate grid cells in the medial entorhinal cortex. The ability to use machine learning models to predict brain representations allows for a deeper understanding of the mechanistic computations of the respective brain areas and a deeper understanding of the nature of the models. However, one of the most exciting and promising new architec…
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The StatQuest Illustrated Guide To Machine Learning
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Imagen - unprecedented photorealism × deep level of language understanding
Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Human raters prefer Imagen over other models (such as DALL-E 2) in side-by-side comparisons, both in terms of sample quality and image-text alignment.
https://gweb-research-imagen.appspot.com/
https://gweb-research-imagen.appspot.com/paper.pdf
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Hi!
As a Machine Learning Engineer, I was studying the design patterns behind scikit-learn's API (you can see here and here) and I was wondering if any of you know of something similar but for R that I can check.
Note: I am asking about R because that's what I am using and it is difficult to find something functional programming oriented for other languages, but any other library you find interesting is welcome!
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Nearly every forward-thinking organization uses analytics in recruitment to bring efficiency to its hiring process. A significant…
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It’s a well-known challenge that large language models (LLMs)—growing in popularity thanks to their adaptability across a variety of applications—carry risks. Because they’re trained on large amounts of data from across the internet, they’re capable of generating inappropriate and harmful language based on similar language encountered during training. Content moderation tools can be deployed to […]
The post (De)ToxiGen: Leveraging large language models to build more robust hate speech detection tools appeared first on Microsoft Research.
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Advances in platform models—large-scale models that can serve as foundations across applications—have significantly improved the ability of computers to process natural language. But natural language processing (NLP) models are still far from perfect, sometimes failing in embarrassing ways, like translating “Eu não recomendo este prato” (I don’t recommend this dish) in Portuguese to “I highly […]
The post Partnering people with large language models to find and fix bugs in NLP systems appeared first on Microsoft Research.
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Electric utilities are taking a course in machine learning to create smarter grids for tough challenges ahead. The winter 2021 megastorm in Texas left millions without power. Grid failures the past two summers sparked devastating wildfires amid California’s record drought. “Extreme weather events of 2021 highlighted the risks climate change is introducing, and the importance Read article >
The post Energy Grids Plug into AI for a Brighter, Cleaner Future appeared first on NVIDIA Blog.
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If someone is curious, I updated the benchmarks after the PyTorch team fixed the memory leak in the latest nightly release May 21->22. The results are quite improved:
https://preview.redd.it/5dkat9hoi3191.png?width=2637&format=png&auto=webp&s=dc42ee03167dd3aefbd0319061994bfc2ff24dab
For a more detailed write-up please see https://sebastianraschka.com/blog/2022/pytorch-m1-gpu.html
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I am curious to understand the reason behind the decision to use Skip-gram rather than CBOW for these two models. According to the original Word2vec paper, CBOW is faster to train and captures syntactic similarities better whereas the skip-gram is slower at training but captures more robust semantic similarities and is also better at handling infrequent words. How does this apply to graph theory and what motivated this decision?
DeepWalk: https://dl.acm.org/doi/abs/10.1145/2623330.2623732
Node2vec: https://dl.acm.org/doi/abs/10.1145/2939672.2939754
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I am pleased to share the news with the ML community that the team I work for recently released a new benchmark: MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
Our paper was accepted at Conference on Health, Inference, and Learning (CHIL) 2022 and published in Proceedings of Machine Learning Research (PMLR).
MedMCQA sample questions
The main contributions:
MedMCQA has More than 194k high-quality Medical entrance exam MCQs.
Dataset requires a deeper domain and language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics.
It Covers 2.4k healthcare topics and 21 medical subjects with an average token length of 12.77, the …
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By 2050, AI will reach remarkable advancements that will be beyond many people's wildest dreams. Robots will not only be able to attain, but also generate, that task in a cost-effective, timely, and meticulous manner, hence increasing efficiency. Read more
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My Dear AI Fellows,
Please check out my latest video about how to control an AGI via Motivation Selection:
https://youtu.be/rLB4xkwgEAw
I also have a lot of great content on the channel regarding life 3.0, building an AGI, AGI Safety, etc. Please check them out and subscribe to my channel!
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Partly inspired by this article: https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/, which describes AI that discovers new biology/physics equations from raw data.
My question is: humans have come a long way from throwing stones to having all the technologies today. This thousands of years of evolution is a process in which new knowledge is developed from existing knowledge–countless cycles of observation, experimentation, and conclusion. Is it possible then, to train an AI to capture this process of generating new knowledge from existing knowledge, and use this AI to fast-forward scientific evolution, thus quickly obtaining future technology that would otherwise take decades to develop?
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https://www.youtube.com/watch?v=0kEqqP8PlUw
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Interested in doing research in neurobiologically-inspired artificial neural networks? Need an open-source, actively maintained tool for reproducing the latest paper on predictive coding or building your own more biologically-faithful neural system? ngc-learn is a recently-released Python library designed in response to these questions.
The ngc-learn dynamics simulator is specifically meant for building, simulating, and analyzing arbitrary predictive coding models based on the neural generative coding (NGC) computational framework and theoretically guided by the free energy principle. This toolkit, distributed under the 3-Clause BSD license, is built on top of Tensorflow 2. Notably, ngc-learn's extensible nodes-and-cables system is general and can even be used to build non-predictive cod…
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Hey Reddit! A heated debate is going on today on the state of the strategic race between the United States and China to dominate in AI. I decided to gather some facts and analyzed publications at ICML 2021 and NeurIPS 2021. Here are the findings -- would love to hear what you think! 🤝❤️🤖
https://thundermark.medium.com/ai-research-rankings-2022-sputnik-moment-for-china-64b693386a4
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Forgive me for sounding irritated, but is there absolutely nothing I can use right now, be it a website, program, mobile app, whatever, that I can just pump some cool stuff out of? Why are we all being shown this amazing technology only to be told "Yo this sick tech exists, but you ain't fuckin using it lol" except for a few select people (e.g. MKBHD's access to dalle 2 for a day)..
I'm not talking about excuses such as nightcafe or wombodream.. I've used them to death and had nothing but quite frankly terrible results and want something that I can pop in terms such as, I don't know, "Drift car nissan silvia" that gives a picture of an actual car for album art etc, or something along those lines, if not just to admire how crazy AI is becoming.
Look, if this stuff existed but was kept completely secret, then the whole 'what you don't know can't hurt you' idea would apply and I would not be pissed off. What is frustrating is that all this wizardry is being flaunted and dangled in our faces whilst also being kept out of reach.
So my initial question still applies, is there anything remotely available right now that can generate images somewhat comparible?
Thanks!
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Terraform Provider Iterative (TPI) is first technological product that simplifies ML training on any cloud as it helps the infrastructure and ML team members save significant time and money in maintaining and configuring the training infrastructure: Iterative adoption of an open-source tool that is the first to train machine learning models on any cloud using Terraform
Terraform provides a flexible CLI service system for managing hundreds of cloud services, and TPI enables data scientists to delegate responsibilities without discovering software.
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The 5th episode of the webinar series on Automated CV Pipelines is coming up! It will be covering automatic instance segmentation and methods to streamline the annotation process.
If you're interested, you can register here!
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There are multiple reasons companies are finding investment opportunities in AI that are going to be beneficial for them in the year 2021. Investment in AI will help a wide range of organizations go through the economic crisis as they emerge from the pandemic. Read more
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With the fast-growing and high-density global air traffic, ensuring efficiency and air transportation safety becomes a critical challenge. AI is already revolutionizing the way air traffic management systems are manufactured and hence is believed to play a key role in optimizing air traffic flow. Read more
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AI advancements in digital technology are growing, and today we have far more technical capability than we had in the 90s, with the potential to expand even quicker in the future. Is, however, the continuance of AI growth in the best interests of humanity? Read more
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Artificial Intelligence is one of the most powerful things humans have been working on for decades, and its limitless magical spells are altering our lives. Read more
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Building next-generation intelligent vehicles requires an AI infrastructure that pushes the cutting edge. Electric vehicle maker NIO is using NVIDIA HGX to build a comprehensive data center infrastructure for developing AI-powered, software-defined vehicles. With high-performance compute, the automaker can continuously iterate on sophisticated deep learning models, creating robust autonomous driving algorithms in a closed-loop environment. Read article >
The post From Cloud to Car: How NIO Develops Intelligent Vehicles on NVIDIA HGX appeared first on NVIDIA Blog.
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Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Study shows AI can identify self-reported race from medical images that contain no indications of race detectable by human experts.
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I would like to create texts with which I can then also shoot a TikTok: this kind of tiktok https://www.youtube.com/watch?v=QEmL-zPBiKs&t=11s: and I don't want to search for texts but create them with an AI right away.
Is it possible?
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https://link.springer.com/article/10.1007/s11948-022-00378-1
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AI systems, like Jasper AI, can write essays on any topic, just with one click- you don’t need to be an expert in writing and stay up late…
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In recent years, social media has become a common means for sharing and consuming news. However, the spread of misinformation and fake news on these platforms has posed a major challenge to the well-being of individuals and societies. Therefore, it is imperative that we develop robust and automated solutions for early detection of fake news […]
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FORMULA 1 (F1) cars are the fastest regulated road-course racing vehicles in the world. Although these open-wheel automobiles are only 20–30 kilometers (or 12–18 miles) per-hour faster than top-of-the-line sports cars, they can speed around corners up to five times as fast due to the powerful aerodynamic downforce they create. Downforce is the vertical force […]
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Since the global financial crisis, risk management has taken a major role in shaping decision-making for banks, including predicting loan status for potential customers. This is often a data-intensive exercise that requires machine learning (ML). However, not all organizations have the data science resources and expertise to build a risk management ML workflow. Amazon SageMaker […]
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Fortnite on GeForce NOW with touch controls on mobile is now available to all members, streaming through the Safari web browser on iOS and the GeForce NOW Android app. The full launch — including the removal of the waitlist — follows a successful beta period in which more than 500,000 participants streamed over 4 million Read article >
The post ‘Fortnite’ Arrives This GFN Thursday With GeForce Performance You Can Touch appeared first on NVIDIA Blog.
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Data management agility has become of key importance to organizations as the amount and complexity of data continues to increase, along with the desire to avoid creating new data silos. The concept of creating a ‘data fabric’ as an agile design concept has been proposed by leading analysts, such as Mark Beyer, Distinguished VP Analyst… Read More »The Foundation of Data Fabrics and AI: Semantic Knowledge Graphs
The post The Foundation of Data Fabrics and AI: Semantic Knowledge Graphs appeared first on Data Science Central.
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Since its inception, algorithmic trading has been a popular strategy for investors. It uses mathematical rules to automate the trading of various assets, such as stocks and futures. However, it has been very challenging for people who don’t have the necessary skills and knowledge — as reported by Psychology Today. According to Nasdaq, one of… Read More »How to Use the Resources of MQL5.community to Empower Your Own Business
The post How to Use the Resources of MQL5.community to Empower Your Own Business appeared first on Data Science Central.
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Keras has launched a computer vision extension package.
Links:
- https://keras.io/keras_cv/
- https://github.com/keras-team/keras-cv/
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https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
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This paper https://paperswithcode.com/paper/handcrafted-localized-phase-features-for claims to achieve 98% top-1 accuracy on kinetics-400 and 96.35 on kinetics-700. From their description, they compute phase-correlation on large patches between consecutive frames and then use that in a knn-classifier. I didn't find any extra info in the paper about the method and frankly I find it hard to believe this beats all of the recent state-of-the art methods.
What do you think? Maybe a (possibly uninteded) foul in the evaluation method?
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According to an article published in Bloomberg,
An Apple Inc. executive who left over the company’s stringent return-to-office policy is joining Alphabet Inc.’s DeepMind unit, according to people with knowledge of the matter.
Ian Goodfellow, who oversaw machine learning and artificial intelligence at Apple, left the iPhone maker in recent weeks, citing the lack of flexibility in its work policies. The company had been planning to require corporate employees to work from the office on Mondays, Tuesdays and Thursdays, starting this month. That deadline was put on hold Tuesday, though.
https://www.bloomberg.com/news/articles/2022-05-17/ian-goodfellow-former-apple-director-of-machine-learning-to-join-deepmind
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I am a student currently studying Software Development at MCAST. For my degree thesis, I am exploring the potential of MIDI sequence generation using Machine Learning techniques.
To evaluate the implemented algorithm, I created a questionnaire which asks respondents to rate 10 different samples.
No personally identifiable information will be collected in this questionnaire.
I would greatly appreciate if you can spare around 5 minutes to take part in this questionnaire.
https://www.survio.com/survey/d/Y1W7D8P1X3J7F8U7Y
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Wow, my blog “Is Data Mesh Fool’s Gold? Creating a Business-centric Data Strategy” created quite a stir. And that was my intention. I actually believe that the Data Mesh is an important data management and governance framework (yes, the Data Mesh is more of a framework than a technology) for helping organizations deliver a business-driven… Read More »Part 2: Is Data Mesh Fool’s Gold? Not if You Avoid the Traps
The post Part 2: Is Data Mesh Fool’s Gold? Not if You Avoid the Traps appeared first on Data Science Central.
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We are happy to invite you to the Gradio Blocks Party - a community event in which we will create interactive demos for state-of-the-art machine learning models. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event will take place from May 17th to 31st. We will be organizing this event on Huggingface: https://huggingface.co/Gradio-Blocks and the Hugging Face discord channel. Prizes will be given at the end of the event, see the Prizes section
We will be building demos using the new Gradio Blocks API. Blocks allows you to build web-based demos in a flexible way using the Gradio library. Gradio is a popular choice for building demos for machine learning models, as it allows you to create web-based UIs all in Python. For example, here is a UI for Dall-E Mini using Gradio Blocks:
https://reddit.com/link/ury6a9/video/p8m2arag24091/player
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https://preview.redd.it/65gejwx5q2091.png?width=1000&format=png&auto=webp&s=107c54464a27b005bf139eabd405134dafe94d15
More like this at: https://www.evilaicartoons.com/
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MIT and Mass General Brigham researchers and physicians connect in person to bring AI into mainstream health care.
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Researchers use artificial intelligence to help autonomous vehicles avoid idling at red lights.
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Real-time rendering is helping one studio take virtual production to impossible heights. In their latest project, the creators at Los Angeles-based company Impossible Objects were tasked with depicting an epic battle between characters from the upcoming video game, Diablo Immortal. But the showdown had to take place on the surface of a Google Pixel phone, Read article >
The post Mission Made Possible: Real-Time Rendering Helps Studio Create Cinematic Battle Between Characters From ‘Diablo Immortal’ appeared first on NVIDIA Blog.
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Eyal Ben-Ari just took his first shot on a goal of bringing professional-class analytics to amateur soccer players. The CEO of startup Track160, in Tel Aviv, has seen his company’s AI-powered sports analytics software tested and used in the big leagues. Now he’s turning his attention to underserved amateurs in the clubs and community teams Read article >
The post AI on the Ball: Startup Shoots Computer Vision to the Soccer Pitch appeared first on NVIDIA Blog.
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Concept artist Pablo Muñoz Gómez dives In the NVIDIA Studio this week, showcasing artwork that depicts a fantastical myth. Gómez, a creator based in Australia, is equally passionate about helping digital artists, teaching 3D classes and running the Zbrush guides website with his creative specialties: concept and character artistry.
The post Concept Artist Pablo Muñoz Gómez Enlivens Fantasy Creatures ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington […]
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PS: This entire article was written by an AI story generator: Jasper AI.
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Most folks think Artificial Intelligence (AI) is a novel notion, although it's been around for a long time. We went back in history and curated a list of all key artificial intelligence breakthroughs that have enabled us to live our current lives. Read more
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CPUs were not as powerful and efficient a few decades ago when it came to running large computations for machine learning. Hardware manufacturers have worked hard to create a processing unit capable of performing any AI operation. Read more
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The release of Generative Pretrained Transformer (GPT-2) has fetched huge attention towards generative language models (LMs), which are pre-trained on massive amounts of unstructured text and have generated efficient results on a variety of NLP applications. LMs can produce texts constantly utilizing a textual prompt’s next-token prediction decoding approach. Models such as CLIP and ALIGN, pre-trained image-text joint embedding approaches, have revived multimodal illustration learning of text and images. Accordingly, it is challenging to integrate the benefits of pre-trained LMs and image-text embedding models to generate visually grounded text. The traditional approaches are generally limited by the object detectors trained with a fixed set of labels. Currently, the ZeroCap approach is ut…
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A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when […]
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Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the amount of research already conducted, it’s difficult to implement due […]
The post FLUTE: A scalable federated learning simulation platform appeared first on Microsoft Research.
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Hi All,
We have released a new version of python library, VevestaX. The library does automatic EDA and experiment tracking in a spreadsheet.
The library can be downloaded using:
pip install vevestaX
Following is the link to its demo:
https://youtu.be/7jmnIOqBpJM
Following is the github link:
https://github.com/Vevesta/VevestaX/blob/main/README.md
Following is the sample output spreadsheet:
https://docs.google.com/spreadsheets/d/15lOXzpcUQtkYQAEnx-YTegvg8zCW6pEK/edit?usp=sharing&ouid=103382336064969333270&rtpof=true&sd=true
Please give us a github star, it would mean the world for us.
Please mail your feature requests to OP at vevestax@vevesta.com
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Machine learning has long aimed to provide models with intelligence comparable to humans. Humans can automatically blend multiple sensory inputs like visual, linguistic, and acoustic signals to generate a complete knowledge of their surroundings by virtue of their intelligence. Even the most robust pre-trained AI models, in contrast to humans, are incapable of doing so, confining themselves to one or two input modalities. Researchers have always been interested in developing effective multimodal learning strategies to support this viewpoint. In their new paper, to further support this idea, the Microsoft Azure Cognitive Services Research team proposes a self-supervised pretraining framework names i-Code: An Integrative and Composable Multimodal Learning Framework.
Continue Reading
Paper: https://arxiv.org/pdf/2205.01818.pdf
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Pre-training of models for NLP applications, exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding.
This paper proposes LayoutLM that jointly model interactions between text and layout information across scanned document images. Fits very well in use-cases like Resume parsing, Bills parsing, Table parsing, etc.
Per Summary: https://youtu.be/ewyDVIdKXm0 Paper Link: https://arxiv.org/abs/1912.13318
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We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up […]
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). In March 2022, we also announced the support for APIs in JumpStart. JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across […]
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I wrote a post arguing that video games are more relevant than ever for AI research. Essentially, RL is at an impasse, and all the really impressive progress comes from self-supervised learning. Could we learn behavior foundation models from millions of traces of real humans playing real games, and would this get us more general and more real intelligence?
https://modl.ai/learning-ai-from-players/
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https://reddit.com/link/uosqgm/video/pxk7h4jb49z81/player
You can try it out in Colab here: https://colab.research.google.com/drive/1E5oU6TjH6OocmvEfU-foJfvCTbTfQrqd?usp=sharing#scrollTo=cVxS_6rBmLKW
To install:
pip install thousandwords
Then in Jupyter Notebook:
from thousandwords import share
Then:
%%share # Your Python code goes here..
More details: https://docs.1000words-hq.com/docs/python-sdk/share
Source: https://github.com/edouard-g/thousandwords
Homepage: https://1000words-hq.com
-------------------------------
EDIT:
Thanks for upvotes and the feedback.
People have voiced their concerns of inadvertent data leaks, and that the Python package wasn't doing enough to warn the user ahead of time.
As a short-term mitigation, I've pushed an update. The %%share magic now warns the user about exactly what gets shared and requires manual confirmation (details below).
We'll be looking into building an option to share privately.
Feel free to ping me for questions/concerns.
More details on the mitigation:
from thousandwords import share x = 1
Then:
In [3]: %%share ...: print(x) This will upload 'x' server-side. Anyone with the link will have read access. Do you wish to proceed ? [y/N]
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When it comes to safety, efficiency and sustainability, autonomous vehicles are delivering a clean sweep. Autonomous vehicle company and NVIDIA Inception member WeRide this month began a public road pilot of its Robo Street Sweepers. The vehicles, designed to perform round-the-clock cleaning services, are built on the high-performance, energy-efficient compute of NVIDIA. The fleet of Read article >
The post Broom, Broom: WeRide Revs Up Self-Driving Street Sweepers Powered by NVIDIA appeared first on NVIDIA Blog.
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https://github.com/microsoft/Swin-Transformer
The ImageNet-22K pretrained Swin-V1-Tiny and Swin-V1-Small models are also released
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nebullvm is an opensource library that generates an optimize version of your deep learning model that runs 2-10 times faster in inference without performance loss by leveraging multiple deep learning compilers (openvino, tensorrt, etc.). And thanks to today's new release, nebullvm can accelerate up to 30x if you specify that you are willing to trade off a self-defined amount of accuracy/precision to get even lower response time and a lighter model. This additional acceleration is achieved by exploiting optimization techniques that slightly modify the model graph to make it lighter, such as quantization, half precision, distillation, sparsity, etc.
The goal of nebullvm is to help other developers benefit from the most advanced inference optimization techniques without having to spend countless hours understanding, installing, testing and debugging these powerful technologies.
Hoping you enjoy the project, and please give feedback if you have any. You can also find more information (benchmarks, tutorials, notebooks) on github! And happy acceleration :)
https://github.com/nebuly-ai/nebullvm
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In the last decade, computer vision use cases have been a growing trend, especially in industries like insurance, automotive, ecommerce, energy, retail, manufacturing, and others. Customers are building computer vision machine learning (ML) models to bring operational efficiencies and automation to their processes. Such models help automate the classification of images or detection of objects […]
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Organizations use agile project management platforms such as Atlassian Jira to enable teams to collaborate to plan, track, and ship deliverables. Jira captures organizational knowledge about the workings of the deliverables in the issues and comments logged during project implementation. However, making this knowledge easily and securely available to users is challenging due to it […]
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This blog post is co-written by Jonathan Lee, Nelson Leung, Paul Min, and Troy Squillaci from Intel. In Part 1 of this post, we discussed how Intel®3DAT collaborated with AWS Machine Learning Professional Services (MLPS) to build a scalable AI SaaS application. 3DAT uses computer vision and AI to recognize, track, and analyze over 1,000 […]
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Many companies are overwhelmed by the abundant volume of documents they have to process, organize, and classify to serve their customers better. Examples of such can be loan applications, tax filing, and billing. Such documents are more commonly received in image formats and are mostly multi-paged and in low-quality format. To be more competitive and […]
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Bengal tigers, red pandas and mountain gorillas are among the world’s most familiar endangered species, but tens of thousands of others — like the Karpathos frog, the Perote deer mouse or the Mekong giant catfish — are largely unknown. Typically perceived as lacking star quality, these species are now roaming massive billboards in one of Read article >
The post Urban Jungle: AI-Generated Endangered Species Mix With Times Square’s Nightlife appeared first on NVIDIA Blog.
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Good. Bad. You’re the Guy With the Gun this GFN Thursday. Get ready for some horrifyingly good fun with Evil Dead: The Game streaming on GeForce NOW tomorrow at release. It’s the 1,300th game to join GeForce NOW, joining on Friday the 13th. And it’s part of eight total games joining the GeForce NOW library Read article >
The post GFN Thursday Gets Groovy As ‘Evil Dead: The Game’ Marks 1,300 Games on GeForce NOW appeared first on NVIDIA Blog.
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AI Weirdness: the strange side of machine learning
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Your new Machine Learning project is about to fail. Yes, you read that right.
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Researchers devise an efficient protocol to keep a user’s private information secure when algorithms use it to recommend products, songs, or shows.
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🙌 Hey all, check out our work on AiMLflow!
We are building a tool that mounts the MLflow logs and enables an Aim-based super-performant UI for metric, image and other ML metadata comparison.
If you are using MLflow, we would love to chat with you and share our progress on the project for feedback.
https://aimstack.io/aimlflow
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In this digital era, several law enforcement agencies across the globe are leveraging artificial intelligence (AI) to resolve more criminal cases in a very short time equipped with AI algorithms developed to identify, locate and arrest the real or potential criminals faster than ever. Read more
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https://blog.r2c.io/ar-vr-the-next-frontier-in-banking-and-financial-services/
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Recently, I am stumbling frequently across this book: https://www.oreilly.com/library/view/analytical-skills-for/9781492060932/. I am thinking about buying it. Are here some people who own this book and can give some recommendations?
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We present an end-to-end deep view aggregation method for 3D semantic segmentation from images and point clouds. We reach SOTA on S3DIS and KITTI360 without requiring point cloud colorization, meshing, or depth sensors: just point clouds, images, and their poses.
preprint | code | paperwithcode
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Hey everyone!
As the title says, I recently wrote a technical article where I built an uplift model to increase marketing ROIs by targeting the right group of people (the persuadables).
Here's the link: https://towardsdatascience.com/targeting-the-right-group-with-uplift-modelling-5682de2dff8b
Curious to know if anyone has successfully used uplift modeling in their industry or field? And let me know what you think. Any feedback is greatly appreciated!
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Paper: https://arxiv.org/abs/2205.05061
Videos: https://www.youtube.com/watch?v=8k9FNxIU0KQ
Github: Coming soon
Playlist: https://www.youtube.com/watch?v=WXMHJszkz6M&list=PL2KGNY2Ei3ix7Vr_vA-ZgCyVfOCfhbX0C
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This blog post is co-written by Rudra Hota and Esaias Pech from Continental AG. Many drivers have had the experience of trying to adjust temperature settings in their vehicle while attempting to keep their eyes on the road. Whether the previous driver preferred a warmer cabin temperature, or you’re now wearing warmer clothing, or the […]
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In Part 6.0 of the Transfer Learning series we have discussed about Mobilenet pre-trained model in depth so in this series we will…
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There’s an underlying theme to this week’s articles, which is a curious occurrence given that so much of our content is user-driven. That theme is the Value of Data. There is a tendency when looking at data in its various incarnations to view all data as somehow being valuable. Realistically, without rolling up sleeves and… Read More »DSC Weekly Newsletter 10 May 2022: Data Meshes, Digital Twins, and Knowledge Graphs
The post DSC Weekly Newsletter 10 May 2022: Data Meshes, Digital Twins, and Knowledge Graphs appeared first on Data Science Central.
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With continuous advancements in Artificial Intelligence (AI), the manufacturers are spearheading to apply of it in their manufacturing processes to boost product quality, operational efficiency, workforce safety, and many more. Read more
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Hi everyone,
Upon reading [PIFuHD: Multi-Level pixel-aligned implicit function for high-resolution 3d human digitization](https://arxiv.org/abs/2004.00452), I recognized that they used [BUFF dataset](https://buff.is.tue.mpg.de/index.html) which is open only for academic purpose.
To access this data, I have to register academic e-mail and send some declaration papers....
I just want to know how big this dataset is, maybe more than TB..?
Does anyone know how big this is?
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Hi everyone
Just wanted to share a really small (micro) python command line utility that interfaces with `pycoco` library that allows you to generate tiff masks from the coco image dataset for training with semantic segmentation (i.e. UNet) where you can also filter by categories. Found it useful if you want to extract specific images from the Coco dataset for your own semantic segmentation project. Tiff images contain pixel values already representing class labels 0, 1, 2 etc... Hope someone else finds it useful!
https://github.com/ralampay/pycocosegmentor
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A thoughtful approach to AI ethics is becoming increasingly important for all organizations deriving value from AI. We hope that by providing an overview of the top toolkits and resources that exist – starting with Fairness and Robustness – will help more companies adopt AI responsibly, with ethical principles at the core. https://www.borealisai.com/en/blog/industry-analysis-ai-fairness-toolkits-landscape/
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The Nodding Pigeon library provides a pre-trained model and a simple inference API for detecting head gestures in short videos. Under the hood, it uses Google MediaPipe for collecting the landmark features.
For ML practitioners, this project is also an example of using generative data from a small base-dataset for model training.
Please take a look! :)
https://github.com/bhky/nodding-pigeon
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Here is a video explaining the model architecture of the DALLE-2 architecture: https://youtu.be/Z8E3LxqE49M
The paper title is, "Hierarchical Text-Conditional Image Generation with CLIP Latents" and the arxiv link to the paper is here: https://arxiv.org/abs/2204.06125
Official website is here: https://openai.com/dall-e-2
Hope its useful.
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Hi guys, I recently created a new repo on Github, it contains a lists of RL agents to solve discrete action space problems like classic control and Atari games. It includes the most recent algorithms from DeepMind like Never Give Up and Agent57 (also not fully tested on Atari games yet because lack of hardware resources). Hope you will find it helpful.
https://github.com/michaelnny/deep_rl_zoo
The post was originally posted on r/reinforcementlearning a few days ago, but I though re-posting here might reach more people, have a good day!
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After talking to many customers recently at Dell Technologies World, I am very, very (very!) concerned how many organizations are putting their Data Strategy success into the hands of Data Meshes. Sorry, but I think the way that IT organizations are thinking about a Data Mesh is fool’s gold. I think the data mesh (along… Read More »Is Data Mesh Fool’s Gold? Creating a Business-centric Data Strategy
The post Is Data Mesh Fool’s Gold? Creating a Business-centric Data Strategy appeared first on Data Science Central.
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Subtitle creation on video content poses challenges no matter how big or small the organization. To address those challenges, Amazon Transcribe has a helpful feature that enables subtitle creation directly within the service. There is no machine learning (ML) or code writing required to get started. This post walks you through setting up a no-code […]
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Human intuition is usually good at dealing with concepts like averages or mean-values, whereas it often performs poorly when it comes to…
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The future of content creation is in AI. This week In the NVIDIA Studio, discover how AI-assisted painting is bringing a new level of inspiration to the next generation of artists.
The post Creator Karen X. Cheng Brings Keen AI for Design ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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We benchmarked on more than 100K series and show that you can improve MAPE forecast accuracy by 17% with 37x less computational time using Nixtlas StatsForecast. That's the difference between paying $10 or $296 on AWS.
It’s time to overcome the false prophets.
Check Nixtla's FB-Prophet adapter: https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter
https://preview.redd.it/fs3zqm4pcjy81.png?width=1280&format=png&auto=webp&s=326c82f04fae7df8934a434ba03fd5e683eadb99
The two lines you need
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The daily volume of third-party and user-generated content (UGC) across industries is increasing exponentially. Startups, social media, gaming, and other industries must ensure their customers are protected, while keeping operational costs down. Businesses in the broadcasting and media industries often find it difficult to efficiently add ratings to content pieces and formats to comply with […]
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User-generated content (UGC) grows exponentially, as well as the requirements and the cost to keep content and online communities safe and compliant. Modern web and mobile platforms fuel businesses and drive user engagement through social features, from startups to large organizations. Online community members expect safe and inclusive experiences where they can freely consume and […]
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Working as an aerospace engineer in Malaysia, Chee How Lim dreamed of building a startup that could really take off. Today his company, Tapway, is riding a wave of computer vision and AI adoption in Southeast Asia. A call for help in 2019 with video analytics led to the Kuala Lumpur-based company’s biggest project to Read article >
The post More Freedom on the Freeway: AI Lifts Malaysia’s Toll Barriers appeared first on NVIDIA Blog.
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Have a question about numerical differential equations? Odds are this CSAIL research affiliate has already addressed it.
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In the previous post, we discussed the various definitions of digital twins and we see that there are no shortage of them! But here is a question we discussed in class Should the definition of Digital twins include simulation of complex systems? In my opinion, simulation is the raison d’être for digital twins let me explain (some of the ideas… Read More »Should the definition of Digital twins include simulation of complex systems?
The post Should the definition of Digital twins include simulation of complex systems? appeared first on Data Science Central.
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Deep learning, natural language processing, data analytics, and big-data mining are fields of Artificial Intelligence (AI), and many companies are looking for professionals in these fields. A professional degree in AI from a reputed university will help you get started in this industry. Read more
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Cognitive biases are an essential and serious issue. Especially for people who deal with data (algorithms).
I've compiled a list (pdf/EPUB) of over 160 biases (mainly from Wikipedia). Maybe this is useful for some.
These biases affect belief formation, reasoning processes, business & economic decisions, and human behavior in general.
Let's learn more about our human biases to make less biased conclusions in the future.
The PDF/EPUB can be downloaded for free on leanpub: Cognitive Biases: A Brief Overview of Over 160 Cognitive Biases
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Federated learning is a machine learning technique that trains a model over several dispersed nodes or hosts, as the name suggests. Each node utilizes its own training data. If the model parameters are shared between nodes rather than the raw data, the data can be kept private.
Due to privacy concerns, obtaining training data to design and evolve machine learning models is increasingly being questioned, and federated learning can help alleviate some of these issues.
The Chinese e-commerce behemoth, Alibaba, has created a federated learning platform that allows machine learning algorithms to be constructed without sharing training data.
Continue Reading
Github: https://github.com/alibaba/FederatedScope
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I'm reading the paper [On the uncertainty principle of neural networks](https://arxiv.org/abs/2205.01493), and i'm doubting one inequality.
In the middle of the equation (7) and (8), there's a part that states
If we use the property ~~~
https://preview.redd.it/tz6k5htt0cy81.png?width=774&format=png&auto=webp&s=99fc8b5701e00fdf253e2f52eb2fde240772fe5e
Is the yellow inequality true? I think the inequality should be opposite, due to arithmetic–geometric mean inequality.
Thks.
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Hello community,
I am a bit baffled scikit-learn does not support this.
I am looking for a good python library that enables fitting a decision tree regressor on both numerical and categorical features (non-binary tree).
Could you point me to one if you know any, please? Thanks!
(PS: This is for visualization and interpretability, so things like catboost won't do)
EDIT: I think I may have found what I was looking for: chefboost (PS: unfortunately it is a bit simplistic and does not support pruning atm)
Also xMattC3 pointed this ongoing PR for scikit learn: NOCATS
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The guide introduces Terraform Provider Iterative (TPI) - an open-source tool extending the functionality of Terraform. The tool enables full lifecycle management of computing resources and is designed specifically for ML pipelines: Machine Learning Workloads with Terraform Provider Iterative
It was designed for machine learning (ML/AI) teams and optimizes CPU/GPU expenses. TPI unifies auto-scaling groups for all the major cloud providers: AWS, Azure, GCP and Kubernetes.
Spot instances auto-recovery (if an instance was evicted/terminated) with data and checkpoint synchronization
Auto-terminate instances when ML training is finished - you won't forget to terminate your expensive GPU instance for a week :)
Using Terraform commands and config (HCL)
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Mοst mοdеrn-day smartphοnеs with grеat camеras makе it rеally еasy fοr anyοnе and еvеryοnе tο click gοοd phοtοs. But what mοst pеοplе dοn’t rеalizе is that with еach phοtοgraph, thеy’rе alsο capturing an lοt οf pеrsοnal infοrmatiοn which, whеn thеy sharе that phοtο οn sοcialmеdia, bеcοmеs availablе tο a much widеrsеt οf pеοplе οn thе… Read More »Understanding EXIF Data and How to View It on Android Phones
The post Understanding EXIF Data and How to View It on Android Phones appeared first on Data Science Central.
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Last few years ago, the industrial revolution is the most popular evolution ever faced by the industrial sector. It encompasses all the latest technology trends which affecting industries over the world. Autonomous cars, smart connected devices, sensors, computer chips, and many other technologies represented this transformation. This happens due to the manufacturing industry has been… Read More »AI In Manufacturing: Know How Latest Intelligence Reshaping the Industries with Speed and Accuracy
The post AI In Manufacturing: Know How Latest Intelligence Reshaping the Industries with Speed and Accuracy appeared first on Data Science Central.
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A rise in netizens and social media platforms, coupled with the growth of the mobile internet, has resulted in a jump in the creation and consumption of User Generated Content (UGC). Social media platforms, in all honesty, have become a major channel for disseminating, circulating, and exchanging information to billions of people on the internet… Read More »Social Media, Cyber Bullying, and Need For Content Moderation
The post Social Media, Cyber Bullying, and Need For Content Moderation appeared first on Data Science Central.
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Data management (DM) discussions can be frustrating because both those feeling the pain and the consultants who try to help them are–90+ percent of the time, it seems–still using the same old ways. Those ways only go so far, and won’t go any farther. That’s because those who reinforce the old ways assume that what… Read More »The long game: Desiloed systems and feedback loops by design (I of II)
The post The long game: Desiloed systems and feedback loops by design (I of II) appeared first on Data Science Central.
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Warning: This is going to get heavy into Turtle code, but I think there’s enough here for it to be worth reading if you are involved in knowledge graph work. I’ve been working with knowledge graphs a lot lately, and a conversation that I had with a few other ontologists has been resonating in my… Read More »The Graph of Thrones: the Now Graph and Eternal Graph in RDF-Star Modeling
The post The Graph of Thrones: the Now Graph and Eternal Graph in RDF-Star Modeling appeared first on Data Science Central.
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In my previous post, I defined an experimentation program (ExPr) as the mechanism by which a company uses randomized controlled experiments to generate positive business results. An ExPr is composed of the people, processes, and infrastructure for running experiments at… Read More
The post Driving Experimentation Forward through a Working Group (Experimentation Program Series: Guide 03) appeared first on ML in Production.
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In my previous post, I defined an experimentation program (ExPr) as the mechanism by which a company uses randomized controlled experiments to generate positive business results. An ExPr is composed of the people, processes, and infrastructure for running experiments at… Read More
The post Driving Experimentation Forward through a Working Group (Experimentation Program Series: Guide 03) appeared first on ML in Production.
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Reinforcement learning has immediate applications in industrial robotics and other control oriented tasks. Are there any interesting real-world applications of RL that is less obvious than robotics or trading? I saw one application in the crypto space and am curious about the other different possible applications (can be in any sector) of RL
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I created this Streamlit website for Jina AI's awesome DALL·E Flow project.
What do you think?
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Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler can simplify your data preparation and feature engineering processes and help you with data selection, cleaning, exploration, and visualization. Data Wrangler has over 300 built-in transforms written in PySpark, […]
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Approximately 7,000 languages are in use today. Despite attempts in the late 19th century to invent constructed languages such as Volapük or Esperanto, there is no sign of unification. People still choose to create new languages (think about your favorite movie character who speaks Klingon, Dothraki, or Elvish). Today, natural language processing (NLP) examples are […]
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In recent years, natural language understanding (NLU) has increasingly found business value, fueled by model improvements as well as the scalability and cost-efficiency of cloud-based infrastructure. Specifically, the Transformer deep learning architecture, often implemented in the form of BERT models, has been highly successful, but training, fine-tuning, and optimizing these models has proven to be […]
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Organizations today have been using some form of document management for years, whether on paper, computer, or online. While we at Bentech…
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In our daily conversations, we come across new words or terms that we may not know. Perhaps these are related to a new domain that we’re just getting familiar with, and we pick these up as we understand more about the domain. For example, home loan terminology (“curtailment”), shortened words, (“refi”, “comps”), and acronyms (“HELOC”) […]
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Understanding customer behavior is top of mind for every business today. Gaining insights into why and how customers buy can help grow revenue. But losing customers (also called customer churn) is always a risk, and insights into why customers leave can be just as important for maintaining revenues and profits. Machine learning (ML) can help […]
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I am a bit confused. In this repository (https://github.com/zoeyuchao/mappo), the authors claim that "by default all experiments assume a shared policy by all agents, i.e. there is one neural net shared by all agents. However, in the code I see that a) there is the option of sharing the observation space b) there is no option of sharing the action space. So what does that sentence mean exactly? Thanks!
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https://youtu.be/Pm93D8CVlY8
Today we build our own AI that can create as many bored apes as we want! Fungibility for everyone!
OUTLINE:
0:00 - Introduction
2:05 - Generative Adversarial Networks
3:40 - Scraping Opensea with BrightData
7:55 - Training the GAN
11:35 - Here are the results!
15:20 - Diving deeper into BrightData
Try the model here: https://huggingface.co/spaces/ykilcher/apes
or here: https://ykilcher.com/apes
Files & Models here: https://huggingface.co/ykilcher/apes/tree/main
Code here: https://github.com/yk/apes-public
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Lensor uses cameras and machine learning to inspect a car in 7 seconds, making over 200 photos. These are checked by a system that was trained on a huge collection of photos of damage. In the future this will replace walking around the car at the rental station, filling in forms.
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Paper: https://arxiv.org/abs/2205.01917
Impressive performance on diverse datasets may indicate higher generalizability :) [Without \"task-specific\" customizations]
Confirms that multi-modal models can scale further from single-digit Billion params (who would've thought) and scales up an simple CLIP-like model showing substantial improvements - especially in 0-shot domain. Simple Contrastive learning appears more and more promising for multi-modal objectives...
Overall, its nothing novel - simply scaled up research.
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Teaching the AI brains of autonomous vehicles to understand the world as humans do requires billions of miles of driving experience. The road to achieving this astronomical level of driving leads to the virtual world. On the latest episode of the AI Podcast, Waabi CEO and founder Raquel Urtasun joins NVIDIA’s Katie Burke Washabaugh to Read article >
The post Driver’s Ed: How Waabi Uses AI, Simulation to Teach Autonomous Vehicles to Drive appeared first on NVIDIA Blog.
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Enjoy the finer things in life. May is looking pixel perfect for GeForce NOW gamers. RTX 3080 members can now take their games to the next level, streaming at 4K resolution on the GeForce NOW PC and Mac native apps — joining 4K support in the living room with SHIELD TV. There’s also a list Read article >
The post GFN Thursday Caught in 4K: 27 Games Arriving on GeForce NOW in May, Alongside 4K Streaming to PC and Mac Apps appeared first on NVIDIA Blog.
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Technological innovation that enables scaling of quantum computing underpins the Microsoft Azure Quantum program. In March of this year, we announced our demonstration of the underlying physics required to create a topological qubit—qubits that are theorized to be inherently more stable than existing ones without sacrificing size or speed. However, our quest to deliver a […]
The post Azure Quantum innovation: Efficient error correction of topological qubits with Floquet codes appeared first on Microsoft Research.
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Do you want to reduce costs and introduce more efficient workflows in your company? Then you may have thought about using artificial…
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One of the more frustrating side effects of the Long Pandemic has been the rise in virtual meetings. I recently was talking with a colleague when the subject of meetings came up. “I swear that I spend much of most days in meetings,” she bemoaned. “It wouldn’t be so bad, but so many of them… Read More »DSC Weekly Newsletter 03 May 2022: How Many Meetings Do We Need?
The post DSC Weekly Newsletter 03 May 2022: How Many Meetings Do We Need? appeared first on Data Science Central.
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Hi there!
Clubhouse shared a case-study on how it ranked recommendations for real-time, short-lived rooms in its Hallway with GBDTs and fast features. Thought you would find it interesting!
Post here: https://blog.clubhouse.com/making-the-hallway-more-relevant-with-machine-learning/
Live Q&A: https://www.clubhouse.com/event/MOp41drX?utm_medium=ch_event&utm_campaign=sI95qy9i-EC5I3MvlueR7g-176282
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Hi everyone,
I would like to share a blog post explaining Yaetos, an open source data framework I created some time ago and used in previous companies : https://medium.com/@arthurprevot/yaetos-data-framework-description-ddc71caf6ce . It is meant for data scientists, engineers and analysts to create and schedule data pipelines and ML models in the AWS cloud.
Any feedback on the tool or the article is welcome ! Thanks !
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Hello Reddit, I am writing this post to make some personal promotion of a Kaggle competition that I built to test the DVC framework.
The goal of the competition is to predict the interest (combination of clicks,views and age) that an image can bring.
Competition : https://www.kaggle.com/competitions/be-honest-what-do-you-think-of-this-image
Have fun (Any feeback is welscome)
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Intelligence measures how quickly a person can adjust to a new situation using only a few simple instructions. Despite the contrasts between the two, children may recognize real animals in the zoo after seeing a few photographs of the animals in a book. On the other hand, Typical visual models do not yet reflect this level of human intellect. They need to be trained on tens of thousands of examples that have been explicitly annotated for that task. If the goal is to count and identify animals in an image, such as “three zebras,” thousands of photographs must be collected and each image annotated with their numbers and species. The requirement to train a new model each time it is confronted with a new job is the most predominant drawback, making the process inefficient, costly, and resource…
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AWS customers are relying on Infrastructure as Code (IaC) to design, develop, and manage their cloud infrastructure. IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible, while being able to follow best practices in the area of development operations (DevOps). One possible approach to manage AWS infrastructure and services with IaC is […]
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Talking about the main Computer Vision development services and identifying key activities, goals, and outcomes to simplify…
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NVIDIA’s latest academic collaborations in graphics research have produced a reinforcement learning model that smoothly simulates athletic moves, ultra-thin holographic glasses for virtual reality, and a real-time rendering technique for objects illuminated by hidden light sources. These projects — and over a dozen more — will be on display at SIGGRAPH 2022, taking place Aug. Read article >
The post Setting AIs on SIGGRAPH: Top Academic Researchers Collaborate With NVIDIA to Tackle Graphics’ Greatest Challenges appeared first on NVIDIA Blog.
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https://youtu.be/ckIIxKM14Ow
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Hey there!
We're happy to announce that we just published the first Unit of Deep Reinforcement Learning Class) 🥳
In this Unit,you'll learn the foundations of Deep RL. And you’ll train your first lander agent🚀 to land correctly on the moon 🌕 using Stable-Baselines3 and share it with the community.
You’ll be able to compare the results of your LunarLander-v2 with your classmates using the leaderboard 🏆 👉 https://huggingface.co/spaces/ThomasSimonini/Lunar-Lander-Leaderboard
1️⃣ The introduction to deep learning article 👉 https://huggingface.co/blog/deep-rl-intro
2️⃣ The hands-on 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/unit1.ipynb
3️⃣ The leaderboard 👉 https://huggingface.co/spaces/ThomasSimonini/Lunar-Lander-Leaderboard
If you have questions and feedback I would love to answer,
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Author: Bokang Zhang, Chenhao Huang Post Summary: Nowadays, many Database-as-a-Service (DBaaS) solutions separate the computation layer and the storage layer. These include, for example, Amazon Aurora and Google BigQuery. This solution is attractive, as the data storage and data replication can be handled by existing services. DBaaS takes off the need to worry about this… Read More »Improve Performance and Data Availability with Elastic Block Store (EBS)
The post Improve Performance and Data Availability with Elastic Block Store (EBS) appeared first on Data Science Central.
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Your regulatory data project likely has no use case for design-time data lineage. tl/dr Mapping Data Lineage at design time, for its own end, has no regulatory use case or ROI. Buying a specialist tool to support that mapping has even less ROI. Regulations see that kind of documentary data lineage as ancillary at best.… Read More »Do regulatory data projects really need design-time data lineage? Probably not.
The post Do regulatory data projects really need design-time data lineage? Probably not. appeared first on Data Science Central.
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My mentors and compadres at Semantic Arts hold a Data-Centric Architecture Forum (DCAF) every year in Fort Collins, Colorado. It’s a chance for technically inclined data and data modeling enthusiasts to brainstorm on the gaps in the architecture and how to fill those gaps. This year’s DCAF takes place on June 6th – 8th, 2022.… Read More »Seven data-centric architecture innovations that businesses can’t afford to overlook
The post Seven data-centric architecture innovations that businesses can’t afford to overlook appeared first on Data Science Central.
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Now I shall sing the second kingdom there where the soul of man is cleansed, made worthy to ascend to Heaven. Dante Alighieri, The Divine Comedy, Purg. I.4–9 In its most recent (April 2022) Data and Analytics Trends report, Gartner made a number of good points in and around the subject of AI and how… Read More »Today’s Data Purgatory and a History Lesson from the Energy Industry
The post Today’s Data Purgatory and a History Lesson from the Energy Industry appeared first on Data Science Central.
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We have designed a lab environment to do some tests on some devices. One of the devices that we had tested was a WD My Book World Edition 1TB (White Light). When we plugged in the power cord, it sounded like it started up fine however, the web interface was not accessible and there was… Read More »Handling SQL Server Database Corruption When Your File System Fails
The post Handling SQL Server Database Corruption When Your File System Fails appeared first on Data Science Central.
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Green banking's a way of banking that aims for sustainability and responsibility in order to protect the environment.
The post Green Banking for Sustainability and Responsible Environmental Protection appeared first on Data Science Central.
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A machine-learning model can identify the action in a video clip and label it, without the help of humans.
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Diffusion models (DMs) have a more stable training phase than GANs and less parameters than autoregressive models, yet they are just really resource intensive. The most powerful DMs require up to a 1000 V100 days to train (that’s a lot of $$$ for compute) and about a day per 1000 inference samples. The authors of Latent Diffusion Models (LDMs) pinpoint this problem to the high dimensionality of the pixel space, in which the diffusion process occurs and propose to perform it in a more compact latent space instead. In short, they achieve this feat by pertaining an autoencoder model that learns an efficient compact latent space that is perceptually equivalent to the pixel space. A DM sandwiched between the convolutional encoder-decoder is then trained inside the latent space in a more computationally-efficient way.
In other words, this is a VQGAN with a DM instead of a transformer (and without a discriminator).
As for the details, let’s dive in, shall we?
Full summary: https://t.me/casual_gan/293
Blog post: https://www.casualganpapers.com/high-res-faster-diffusion-democratizing-diffusion/Latent-Disffusion-Models-explained.html
Latent Diffusion Models
arxiv / code
Join the discord community and follow on Twitter for weekly AI paper summaries!
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https://arxiv.org/abs/2108.09598
Paper looks very promising, what do you think?
Anyone tried SERF yet?
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This week In the NVIDIA Studio, we welcome Yangtian Li, a senior concept artist at Singularity6. Li is a concept designer and illustrator who has worked on some of the biggest video game franchises, including Call of Duty, Magic: the Gathering and Vainglory. Her artwork also appears in book illustrations and magazines.
The post ‘In the NVIDIA Studio’ Welcomes Concept Designer Yangtian Li appeared first on NVIDIA Blog.
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To me it seems to make sense that we would begin relying more and more on AI to improve our old games. For example, ffxiv won't be getting raytracing anytime soon, but I imagine it's only a matter of time before I could download some AI-based image software (like Dall-E) that lets me tweak my gameplay experience to look much more realistic.
Do you think I'm right in assuming this is the likely trajectory of AI's use in game graphics?
And if the answer to 1 is yes, how far away do you think such technology is?
Thank you!
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Artificial intelligence (AI) will continue to disrupt the media sector, just as it did in 2020 and 2021. AI will most likely fulfill the three critical roles of recommendation, speech recognition, and media automation in this market.
Read more: Artificial Intelligence will Continue to Transform the M&E Landscape
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Unsupervised semantic segmentation seeks to uncover and localize semantically significant categories within image corpora without any annotation. However, there are several challenges in creating annotated training data. These challenges frequently often outweigh semantic segmentation methods’ superior accuracy. Algorithms must develop features for every pixel that are both semantically relevant and compact enough to form discrete clusters to extract meaningful categories with any annotation from the training data. A team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Google, and Cornell University has achieved this by creating a machine learning model named STEGO (Self-supervised Transformer with Energy-based Graph Optimization) that surpasses previous methods by decoupling feature learning from cluster compactification.
A frozen backbone makes up STEGO, and it serves as a source of learning feedback and input to the segmentation head for predicting distilled characteristics. This segmentation head is a direct feed-forward network with a ReLU activation function. Unlike earlier studies, the algorithm’s efficiency was increased without retraining or fine-tuning the backbone. The STEGO neural network retrieves global image information by pooling spatial variables in a global average. Then, based on the cosine similarity in the backbone’s feature space, a lookup table is computed for each image’s K-Nearest Neighbours.
Continue Reading
Paper: https://arxiv.org/pdf/2203.08414.pdf
Github: https://github.com/mhamilton723/STEGO
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Machine learning (ML) applications are complex to deploy and often require multiple ML models to serve a single inference request. A typical request may flow across multiple models with steps like preprocessing, data transformations, model selection logic, model aggregation, and postprocessing. This has led to the evolution of common design patterns such as serial inference […]
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Today, we’re releasing a new solution for financial graph machine learning (ML) in Amazon SageMaker JumpStart. JumpStart helps you quickly get started with ML and provides a set of solutions for the most common use cases that can be trained and deployed with just a few clicks. The new JumpStart solution (Graph-Based Credit Scoring) demonstrates […]
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Reading the printed word opens up a world of information, imagination, and creativity. However, scanned books and documents may be difficult for people with vision impairment and learning disabilities to consume. In addition, some people prefer to listen to text-based content versus reading it. A document-to-speech solution extends the reach of digital content by giving […]
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Jack Morrison and Isaac Roberts, co-founders of Replica Labs, were restless two years after their 3D vision startup was acquired, seeking another adventure. Then, in 2018, when Morrison was mowing his lawn, it struck him: autonomous lawn mowers. The two, along with Davis Foster, co-founded Scythe Robotics. The company, based in Boulder, Colo., has a Read article >
The post Mown Away: Startup Rolls Out Autonomous Lawnmower With Cutting-Edge Tech appeared first on NVIDIA Blog.
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Edward McEvenue grew up making claymations in LEGO towns. Now, he’s creating photorealistic animations in virtual cities, drawing on more than a decade of experience in the motion graphics industry.
The post Meet the Omnivore: 3D Artist Creates Towering Work With NVIDIA Omniverse appeared first on NVIDIA Blog.
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I was conducting a Tech Talk at a client when I mentioned the astronomical growth of data at “the edge”; that data creation at the edge is growing almost as fast as that in the cloud according to IDC. This sent a noticeable ripple across the executive team audience. The executives immediately began debating “How… Read More »Business Model Transformation: Keys to Monetizing the Edge
The post Business Model Transformation: Keys to Monetizing the Edge appeared first on Data Science Central.
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Dealing with a whopping amount of data is normal for businesses in any sector these days. Without using this information obtained from various sources, these entities find it hard to analyze various factors and make strategic decisions. The same can be said about the healthcare sector. Especially after the Covid-19 pandemic, the clinics and medical… Read More »How Power BI Applications Are Reshaping The Healthcare Industry
The post How Power BI Applications Are Reshaping The Healthcare Industry appeared first on Data Science Central.
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We are doing a survey related to multi-agent reinforcement learning systems and benchmarks and would love to hear your opinion.
This survey has 3 questions and will take about 10 seconds to complete.
We really appreciate your participation.
The survey URL is here.
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https://youtu.be/yi-A0kWXEO4
This video explains and summarizes the 87 pages long PaLM: Pathways Language Models paper from Google AI’s Pathways. Yes, it is that 540 billion dense parameter model which can explain jokes and is sensitive to chain of thought reasoning.
Paper link: https://arxiv.org/abs/2204.02311
PaLM blog post: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html
Outline:
00:00 DALL-E 2 or PaLM?
01:14 Weights&Biases (Sponsor)
02:25 A brief history of boring large language models
03:43 What is PaLM?
05:11 Training PaLM on all TPUs
08:11 PaLM training data
08:49 What it can do
10:31 Few-shot learning explained
13:20 Explaining jokes and Outlook
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Hi everyone,
I want to share with you an article that I worked on with my colleague about the use cases of human pose estimation. I would love it if you could check it out and share your ideas for using this technology in the comments below.
https://mobidev.biz/blog/human-pose-estimation-technology-guide
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I was reading this paper and in section 3 they claim that agents act simultaneously and there is no notion of turn-taking:
https://arxiv.org/abs/2104.07750
I was wondering how this works. What I'm used to seeing is a for loop in which, one after the other, agents execute the step function and interact with the environment. How does this change if all agents act simultaneously?
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Reinforcement learning (RL) is a machine learning training strategy that rewards desirable behaviors while penalizing undesirable ones. A reinforcement learning agent can perceive and comprehend its surroundings, act, and learn through trial and error in general. Although RL agents can heuristically solve some problems, such as assisting a robot in navigating to a specific location in a given environment, there is no guarantee that they will be able to handle problems in settings they have not yet encountered. The capacity of these models to recognize the robot and any obstacles in its path, but not changes in its surrounding environment that occur independently of the agent, which we refer to as exogenous noise, is critical to their success.
Existing RL algorithms are not powerful enoug…
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www.usage.ai
Usage AI bundles 3-year no-upfront RIs on AWS with guaranteed buyback -- so users get all the savings of 3-year RIs with none of the commitment. I helped engineer the product. Here to answer any questions!
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https://gradio.app/ is for demoing machine learning models
https://reddit.com/link/ueod3x/video/75e1ozmjihw81/player
Prerequisite: Python 3.7+ and that's it!
Quick Start
To get Gradio running with a simple "Hello, World" example, follow these three steps:
Install Gradio from pip.
pip install gradio
Run the code below as a Python script or in a Python notebook (or in a colab notebook).
import gradio as gr def greet(name): return "Hello " + name + "!!" demo = gr.Interface(fn=greet, inputs="text", outputs="text") if __name__ == "__main__": demo.launch()
The interface below will appear automatically within the Python notebook, or pop in a browser on http://localhost:7860 if running from a script.
see more in the getting started guide: https://gradio.app/getting_started/
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Hello everyone! Following this post numpy in fhe we are releasing a new lib that allows popular machine learning frameworks to run over encrypted data: https://github.com/zama-ai/concrete-ml
Currently this supports xgboost and many sklearn models. We also support pytorch to some extent.
We are trying to closely follow sklearn API (when relevant) to make the use easy to machine learning practitioners.
Happy to hear any feedback on this !
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Hey r/MachineLearning,
I'm Derrick from Layer (layer.ai) - the collaboration-first machine learning platform that enables you to build, train, track, and share your ML projects simply with a few lines of code.
We are soft-launching today! I’ve been working on Layer for the past 2 years with an awesome team around the world. We really poured our hearts and minds into Layer and hope you will like it. Your feedback would be very appreciated!
Layer Demo
To get started, you can simply run our Quickstart Example!
How is Layer different from other tools?
Although there are plenty of ML and DS tooling products, we believe that there is still a large gap around collaboration. Many data science projects are hosted on GitHub, which, in our experience, does not provide sufficient depth and abs…
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Their model’s predictions should help researchers improve ocean climate simulations and hone the design of offshore structures.
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"The Megalodon was a large bivalve, measuring up to 2.5 meters in length. Its shell was covered in spines, and it had a large, powerful jaw for crushing prey."
Although the megalodon is the most widely known as a giant prehistoric shark, I recently learned that Megalodon
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AI Weirdness: the strange side of machine learning
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Terraform Provider Iterative (TPI) address the specific needs of machine learning teams - it is an open-source tool extending the functionality of Terraform, the world's most widely used multi-cloud provisioning product. The tool enables full lifecycle management of computing resources and is designed specifically for machine learning pipelines: Terraform plugin for machine learning workloads: spot instance recovery & auto-termination | AWS, GCP, Azure, Kubernetes
The tool aims to bridge the gap between devops and data science teams and build on top of Terraform, a tool universally familiar to devops teams, but extend it to suit machine learning needs. It provides to following advantages for your ML workflow:
Lower cost: use your preferred cloud provider's existing pricing, including on-demand per-second billing and bulk discounts.
Auto-recovery: spot/preemptible instances are cheap but unreliable. TPI reliably and automatically respawns such interrupted instances, caching & restoring the working directory in the cloud even when you are offline.
Custom spec: full control over hardware & software requirements via a single config file.
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Hi folks, our blog post on how to automatically find label errors in audio datasets has just gone live. We cover the steps to:
⛏️ Perform feature extraction (aka embeddings) on the Spoken Digit dataset with a pre-trained PyTorch model.
🔢 Use cross-validation to generate out-of-sample predicted probabilities for every example in the dataset.
🏷️ Run one line of cleanlab code on these predicted probabilities to identify which audio clips may be mislabeled.
📰 Blog Post + Google Colab: https://cleanlab.ai/blog/label-errors-audio-datasets/
https://preview.redd.it/vpzhwg6jebw81.png?width=1260&format=png&auto=webp&s=3fa79d8a097ae5936e5e6a51e87508adf6190835
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Automated CV Pipelines 4th part is open for registration. It will be covering some of the best practices for video-specific annotation tasks.
If you are interested you can check out the details here!
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Today, AWS announced the general availability of Amazon Rekognition Streaming Video Events, a fully managed service for camera manufacturers and service providers that uses machine learning (ML) to detect objects such as people, pets, and packages in live video streams from connected cameras. Amazon Rekognition Streaming Video Events sends them a notification as soon as […]
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3xLOGIC is a leader in commercial electronic security systems. They provide commercial security systems and managed video monitoring for businesses, hospitals, schools, and government agencies. Managed video monitoring is a critical component of a comprehensive security strategy for 3xLOGIC’s customers. With more than 50,000 active cameras in the field, video monitoring teams face a daily […]
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Abode Systems (Abode) offers homeowners a comprehensive suite of do-it-yourself home security solutions that can be set up in minutes and enables homeowners to keep their family and property safe. Since the company’s launch in 2015, in-camera motion detection sensors have played an essential part in Abode’s solution, enabling customers to receive notifications and monitor […]
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Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Data Wrangler, you can select and query data with just a few clicks, quickly transform data with over 300 built-in data transformations, and understand your data with built-in visualizations without writing any code. Additionally, […]
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Searchmetrics is a global provider of search data, software, and consulting solutions, helping customers turn search data into unique business insights. To date, Searchmetrics has helped more than 1,000 companies such as McKinsey & Company, Lowe’s, and AXA find an advantage in the hyper-competitive search landscape. In 2021, Searchmetrics turned to AWS to help with […]
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Identifying paraphrased text has business value in many use cases. For example, by identifying sentence paraphrases, a text summarization system could remove redundant information. Another application is to identify plagiarized documents. In this post, we fine-tune a Hugging Face transformer on Amazon SageMaker to identify paraphrased sentence pairs in a few steps. A truly robust […]
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This is a guest post by Moovit’s Software and Cloud Architect, Sharon Dahan. Moovit, an Intel company, is a leading Mobility as a Service (MaaS) solutions provider and creator of the top urban mobility app. Moovit serves over 1.3 billion riders in 3,500 cities around the world. We help people everywhere get to their destination […]
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Featuring stunning visuals from futuristic interstellar worlds, including colossal sand creatures, Dune captivated audiences around the world. The sci-fi film picked up six Oscars last month at the 94th Academy Awards, including for Best Sound and Visual Effects. Adapted from Frank Herbert’s 1965 novel of the same name, Dune tells the story of Paul Atreides, Read article >
The post How DNEG Helped Win Another Visual-Effects Oscar by Bringing ‘Dune’ to Life With NVIDIA RTX appeared first on NVIDIA Blog.
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It’s a jam-packed GFN Thursday. This week brings the popular, free-to-play, action role-playing game Lost Ark to gamers across nearly all their devices, streaming on GeForce NOW. And that’s not all. GFN Thursday also delivers an upgraded experience in the 2.0.40 update. M1-based MacBooks, iMacs and Mac Minis are now supported natively. Plus, membership gift Read article >
The post Your Odyssey Awaits: Stream ‘Lost Ark’ to Nearly Any Device This GFN Thursday appeared first on NVIDIA Blog.
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Workshop hosted by MIT’s Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet.
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Competitive seed grants launch yearlong investigations of novel hypotheses about potential causes, biomarkers, treatments of Alzheimer’s and ALS.
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I've made surgeon https://github.com/archinetai/surgeon-pytorch, a small library to inspect the intermediate output layers of pyTorch models without changing the original implementation.
This can be very useful if you are using pre-trained models (e.g. from Huggingface or torch.hub) and want to get embeddings, attention matrices, or simply debug the model without adding additional code – which is often hard to do without changing the implementation.
I hope this can be helpful to anyone!
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I stumbled upon GFlow Net and in my opinion, it looks very similar to diffusion models. There is a touch of RL in GFlow Net but the main idea is very similar to diffusion models. is that right? or am I missing something?
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Hello everyone,
this post is about generative models! (i.e. Score-based-generative models, GANs, etc.)
on leaderboards like this https://paperswithcode.com/sota/image-generation-on-cifar-10
How do they check if the models do not just memorize the training examples? The FID score would be optimal in case you would just generate training examples again.
Best
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I was playing around with several AI models and this sort of stuff happens all the time.
https://preview.redd.it/6arq56nih3w81.png?width=1163&format=png&auto=webp&s=5ca68dce977ae671b0b928c2f1ec3cdd8478257f
I decided to prepare a compilation and rank the answers. Can you help me with deciding how bad or good are some of the answers by taking a survey here?
Edit: I apologize if some of you felt tricked into completing the survey by the original version of the post. It does have about 50 questions but they are mostly just yes/no, good/bad, so it shouldn't take longer than 10 minutes. I intend to write a piece about how AI assistants are doing and prepare a compilation of AI fails. I will share the results :)
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Linking techniques from machine learning with advanced numerical simulations, MIT researchers take an important step in state-of-the-art predictions for fusion plasmas.
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A hundred and forty turbines in the North Sea — and some GPUs in the cloud — pumped wind under the wings of David Standingford and Jamil Appa’s dream. As colleagues at a British aerospace firm, they shared a vision of starting a company to apply their expertise in high performance computing across many industries. Read article >
The post Answers Blowin’ in the Wind: HPC Code Gives Renewable Energy a Lift appeared first on NVIDIA Blog.
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Entrepreneur Jason Mars calls conversation our “first technology.” Before humans invented the wheel, crafted a spear or tamed fire, we mastered the superpower of talking to one another. That makes conversation an incredibly important tool. But if you’ve dealt with the automated chatbots deployed by the customer service arms of just about any big organization Read article >
The post What Is Conversational AI? ZeroShot Bot CEO Jason Mars Explains appeared first on NVIDIA Blog.
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TinyML is a groundbreaking technology! Possessing a lot of potential it is sure to grow exponentially in the coming years.
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Hey, we've been building Baseten to be able quickly deploy models, backends and frontends. I'd love to get your feedback.
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We've recently added Hugging Face support to https://github.com/graphsignal/graphsignal profiler, which I'd like to share in case someone finds it useful in their efforts to optimize speed and compute. More details, code and screenshots in the blog post https://graphsignal.com/blog/benchmarking-and-profiling-hugging-face-training-with-graphsignal/.
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Researchers from MIT/Meta recently released a new framework for unsupervised sentence embedding.
The performance seems to be better than SimCSE, the previous SOTA, by 2.3 absolute points on downstream tasks.
The pretrained models are available on Huggingface. GitHub: https://github.com/voidism/DiffCSE arXiv: https://arxiv.org/abs/2204.10298
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In this post, we demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds over open-source Kubeflow through the integration of highly optimized, cloud-native, enterprise-ready AWS services. Kubeflow is the open-source machine learning (ML) platform dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable. Kubeflow provides many […]
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In this post, we walk you through two sampling techniques in Amazon SageMaker Data Wrangler so you can quickly create processing workflows for your data. We cover both random sampling and stratified sampling techniques to help you sample your data based on your specific requirements. Data Wrangler reduces the time it takes to aggregate and […]
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The adoption of AWS cloud technology at NatWest Group means moving our machine learning (ML) workloads to a more robust and scalable solution, while reducing our time-to-live to deliver the best products and services for our customers. In this cloud adoption journey, we selected the Customer Lifetime Value (CLV) model to migrate to AWS. The […]
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This is the third post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. This post is intended for data scientists, MLOps engineers, and data engineers who are interested in building ML pipeline templates with Amazon SageMaker. […]
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This is the second post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. In this post, we share how the NatWest Group utilized AWS to enable the self-service deployment of their standardized, secure, and compliant MLOps […]
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This is the first post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS to build a scalable, secure, and sustainable machine learning operations (MLOps) platform. This initial post provides an overview of the AWS and NatWest Group joint team implemented Amazon SageMaker Studio as the standard for […]
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Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that helps data scientists and data engineers quickly and easily prepare data for machine learning (ML) applications using a visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Today, […]
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Data scientists face a problem: machine learning models need to be trained on labeled datasets, but labeling the data is tedious and time-consuming. Enter automatic data labeling, in which most of the preprocessing work is done by a computer. At first glance, automatic data labeling sounds too good to be true. Of course, more automation… Read More »2 Ways in Which Automatic Data Labeling Saves Time and Costs
The post 2 Ways in Which Automatic Data Labeling Saves Time and Costs appeared first on Data Science Central.
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Yes and no. So, if you asked this question, good one! When I was new to this stuff, I had the same question and searched up a lot about it.
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